66 citations as recorded by crossref.

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16. Assessment of microscale economic flood losses in urban and agricultural areas: case study of the Santa Bárbara River, Ecuador J. Pinos et al. 10.1007/s11069-020-04084-8
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21. The role of socio-economic and property variables in the establishment of flood depth-damage curve for the data-scarce area in Malaysia S. Sulong & N. Romali 10.1080/1573062X.2022.2099292
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23. Machine learning models to predict myocardial infarctions from past climatic and environmental conditions L. Marien et al. 10.5194/nhess-22-3015-2022
24. Probabilistic Flood Loss Models for Companies L. Schoppa et al. 10.1029/2020WR027649
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26. Bayesian Data-Driven approach enhances synthetic flood loss models N. Sairam et al. 10.1016/j.envsoft.2020.104798
27. Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
28. Evaluating the spatial application of multivariable flood damage models R. Paulik et al. 10.1111/jfr3.12934
29. Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. 10.1029/2017WR022233
30. Flood Vulnerability Models and Household Flood Damage Mitigation Measures: An Econometric Analysis of Survey Data T. Endendijk et al. 10.1029/2022WR034192
31. An empirical flood fatality model for Italy using random forest algorithm M. Yazdani et al. 10.1016/j.ijdrr.2023.104110
32. A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
33. Expert-based versus data-driven flood damage models: A comparative evaluation for data-scarce regions M. Malgwi et al. 10.1016/j.ijdrr.2021.102148
34. Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
35. Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing H. Lv et al. 10.1016/j.scitotenv.2021.145649
36. A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment J. Carreau & V. Guinot 10.1016/j.advwatres.2020.103821
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38. The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index L. Scheiber et al. 10.5194/nhess-23-2313-2023
39. Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka D. Wagenaar et al. 10.1016/j.ijdrr.2019.101162
40. Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. 10.3390/jmse11101960
41. The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model H. Lv et al. 10.1016/j.jhydrol.2020.125797
42. Systemic Financial Risk Arising From Residential Flood Losses H. Thomson et al. 10.1029/2022EF003206
43. The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0 M. Di Bacco et al. 10.5194/nhess-24-1681-2024
44. A machine learning-based prediction and analysis of flood affected households: A case study of floods in Bangladesh K. Ganguly et al. 10.1016/j.ijdrr.2018.12.002
45. Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
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47. A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis R. Harris et al. 10.1016/j.crm.2022.100410
48. Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg F. Brill et al. 10.5194/nhess-24-4237-2024
49. Understanding flood risk in urban environments: spatial analysis of building vulnerability and hazard areas in the Lisbon metropolitan area P. Santos et al. 10.1007/s11069-024-06731-w
50. A data-mining approach towards damage modelling for El Niño events in Peru F. Brill et al. 10.1080/19475705.2020.1818636
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52. Improving flood impact estimations T. Sieg & A. Thieken 10.1088/1748-9326/ac6d6c
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54. Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel 10.1016/j.procs.2020.06.011
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56. Validation of flood risk models: Current practice and possible improvements D. Molinari et al. 10.1016/j.ijdrr.2018.10.022
57. Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties H. Lv et al. 10.1029/2021WR031552
58. Probabilistic framework for quantifying infrastructure systems’ resilience against floods A. Daneshifar & H. Kashani 10.1080/23789689.2024.2328977
59. Residential building and sub-building level flood damage analysis using simple and complex models R. Paulik et al. 10.1007/s11069-024-06756-1
60. Preface: Damage of natural hazards: assessment and mitigation H. Kreibich et al. 10.5194/nhess-19-551-2019
61. Flood Risk Modeling under Uncertainties: The Case Study of Croatia T. Kekez et al. 10.3390/w14101585
62. Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. 10.1029/2018EF001074
63. A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood M. Malgwi et al. 10.1007/s11069-021-04756-z
64. Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
65. Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T. Sieg et al. 10.1029/2018EF001122
66. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019